Covid Report for Family

Background

Notes and format last updated May 7, 2020

Starting on the May 7th update, the NY Times began including probable covid cases/deaths along with confirmed. This mostly affects death counts – for certain geographies that include probable COVID deaths in addition to confirmed, these are now added to the totals. For the time being, they were all added to the May 6th totals, causing a big spike at the U.S. level. Over time, NY Times will revise their historical counts and distribute these added deaths when they actually occurred, so the spike should fade.

• This page shows COVID data broken down into regions that affect our family. It pulls from publicly available raw data from the NY Times, which is reported daily. It’s on a one-day lag, so when you see this report, the most recent day included will be that reported the day prior.
• The table of contents above summarizes the major sections and can be used to jump to those sections.
• Each major section is then divided into tabs to allow you to more easily shuffle between geographies and metrics.
  • If you open on your phone, I don’t believe the tabs will work, so you’ll just see the page as one long, continuous document.
• More detailed descriptions and context can be found in each section.

Growth rates

Heat maps

• The two heat maps below compare how quickly total cases or deaths have grown at various times in our respective geopgraphies.
• The first plot compares growth rate for total cases; the second, growth rate for total deaths.
• The metric used is doubling time, by which I mean how quickly total cases or deaths are doubling.
• The plots track that doubling time at each date for our geographies. Darker colors reflect shorter doubling times, and thus periods of faster growth.
  • You can use the plots to track each geography over time and to compare the geographies to one another.
  • You can also compare the cases and death charts, to see how faster periods of death growth follow faster periods of case growth.

Case growth rates

• This section charts the growth rate of both total and new cases for each of our respective geographies. Each geography has its own chart, and then that chart will have a trendline for total cases and new cases.
  • There are only plots for the U.S. and states because the numbers for the counties are too small to generate worthwhile trendlines in this section.
• Note that we’re charting growth rate and not a count of cases, so don’t think of these as the standard “curve” that we hear about in the news and that we want to flatten. Instead, these growth rate charts help track more precisely what we can only estimate when we see those other curves. For these growth rate charts, if the line is above zero, the metric we are tracking (total or new cases) is continuing to grow. If the growth rate line is going up, it’s growing more quickly each day; if it’s going down but still above zero, it’s growing less quickly (but still growing). Only when the growth rate lines go below zero has the metric stopped growing.
• Each of these two lines uses rolling windows to calculate a growth rate for that particular metric. I do the calculation differently for each to smooth out some of the large day-to-day discrepancies in new case reporting at the state level.
  • For total cases, the trendlines are a rolling 3-day average of daily growth rates in total cases. We want to see these decline (and almost all are), but they can’t go below zero. This is because we’re tracking growth rate and a growth rate line below zero would mean total cases have gone down, which can’t happen. They can only grow less quickly, which means we want to see the total case line get as close to zero as possible.
  • For new cases, the trendlines show a rolling 3-day average of daily growth rate in the rolling 7-day average of new cases. Including two rolling periods in this average helps smooth out crazy spikes at the state level that result from large day-to-day changes. Unlike the lines for total cases, we want to watch for the lines for new cases to get consistently below zero and stay there. That means that we are consistently seeing fewer new cases on a daily basis.

U.S.

Our states

Death growth rates

• This section charts the growth rate of both total and new deaths for each of our respective geographies. Each geography has its own chart, and then that chart will have a trendline for total deaths and new deaths.
  • There are only plots for the U.S. and states because the numbers for the counties are too small to generate worthwhile trendlines in this section.
• Note that we’re charting growth rate and not a count of deaths, so don’t think of these as the standard “curve” that we hear about in the news and that we want to flatten. Instead, these growth rate charts help track more precisely what we can only estimate when we see those other curves. For these growth rate charts, if the line is above zero, the metric we are tracking (total or new deaths) is continuing to grow. If the growth rate line is going up, it’s growing more quickly each day; if it’s going down but still above zero, it’s growing less quickly (but still growing). Only when the growth rate lines go below zero has the metric stopped growing.
• Each of these two lines uses rolling windows to calculate a growth rate for that particular metric. I do the calculation differently for each to smooth out some of the large day-to-day discrepancies in new death reporting at the state level.
  • For total deaths, the trendlines are a rolling 3-day average of daily growth rates in total deaths. We want to see these decline (and almost all are), but they can’t go below zero. This is because we’re tracking growth rate and a growth rate line below zero would mean total deaths have gone down, which can’t happen. They can only grow less quickly, which means we want to see the total death line get as close to zero as possible.
  • For new deaths, the trendlines show a rolling 3-day average of daily growth rate in the rolling 7-day average of new deaths. Including two rolling periods in this average helps smooth out crazy spikes at the state level that result from large day-to-day changes. Unlike the lines for total deaths, we want to watch for the lines for new deaths to get consistently below zero and stay there. That means that we are consistently seeing fewer new deaths on a daily basis.

U.S.

Our states

By population rankings

This section tracks metrics for states and counties normalized for population (number of cases or deaths per million residents), and then compares these figures both for our geographies and the country overall.

States

• This section shows tables ranking all 50 states for per populations rates of total cases, new cases, total deaths, and new deaths.
• For each metric, in addition to the tables, the trends for the top states are plotted over time.
  • We only plot the top ten states for each metric so that the plots aren’t too crowded. But you can view the full 50-state rankings in the tables.

Total confirmed cases

Table of total confirmed cases per million residents (all 50 states)

Ranking	State	Cases Per Million
1	North Dakota	112,442
2	South Dakota	98,385
3	Iowa	78,589
4	Wisconsin	76,945
5	Nebraska	73,778
6	Utah	68,613
7	Montana	64,994
8	Wyoming	64,750
9	Idaho	64,021
10	Minnesota	63,703
11	Illinois	63,589
12	Rhode Island	63,308
13	Kansas	61,023
14	Tennessee	59,326
15	Indiana	58,739
16	Arkansas	57,765
17	Nevada	56,506
18	Alabama	56,425
19	Mississippi	56,423
20	Missouri	56,401
21	Oklahoma	55,771
22	Louisiana	54,758
23	New Mexico	52,434
24	Arizona	52,160
25	Alaska	51,839
26	Florida	49,994
27	Texas	47,315
28	Kentucky	47,056
29	Colorado	46,951
30	Georgia	46,485
31	South Carolina	46,021
32	Michigan	44,308
33	Ohio	43,631
34	New Jersey	42,657
35	Delaware	42,581
36	Connecticut	38,778
37	North Carolina	38,569
38	Massachusetts	38,222
39	New York	37,422
40	Maryland	36,435
41	California	36,075
42	Pennsylvania	34,567
43	District of Columbia	33,424
44	West Virginia	31,838
45	Virginia	30,780
46	Puerto Rico	28,785
47	Washington	25,579
48	Oregon	20,646
49	New Hampshire	19,579
50	Hawaii	13,215
51	Maine	10,451
52	Vermont	8,301

New confirmed cases

Table of new cases per million residents: rolling 3-day average (all 50 states)

Ranking	State	New Cases Per Million
1	Rhode Island	1,551
2	North Dakota	1,390
3	Ohio	1,214
4	Connecticut	985
5	Wyoming	883
6	Indiana	874
7	Idaho	866
8	Nevada	850
9	Arizona	846
10	Alaska	838
11	Minnesota	822
12	Delaware	810
13	Tennessee	797
14	Utah	741
15	Montana	740
16	Nebraska	715
17	Colorado	709
18	Kansas	704
19	California	694
20	Pennsylvania	688
21	South Dakota	653
22	West Virginia	648
23	Illinois	640
24	Oklahoma	626
25	Missouri	625
26	Alabama	617
27	New Hampshire	609
28	Kentucky	604
29	Wisconsin	577
30	New Jersey	565
31	Michigan	558
32	Massachusetts	552
33	Arkansas	545
34	Louisiana	529
35	Mississippi	500
36	North Carolina	500
37	South Carolina	499
38	New Mexico	491
39	Iowa	482
40	Washington	468
41	New York	464
42	Virginia	451
43	Texas	438
44	Maryland	426
45	Georgia	390
46	Florida	374
47	District of Columbia	338
48	Oregon	303
49	Puerto Rico	290
50	Maine	228
51	Vermont	152
52	Hawaii	53

Total deaths

Table of total deaths per million residents (all 50 states)

Ranking	State	Deaths Per Million
1	New Jersey	1,961
2	New York	1,784
3	Massachusetts	1,606
4	Connecticut	1,470
5	Louisiana	1,430
6	North Dakota	1,396
7	Rhode Island	1,387
8	Mississippi	1,349
9	South Dakota	1,255
10	Illinois	1,135
11	Michigan	1,063
12	District of Columbia	993
13	Arizona	959
14	Indiana	952
15	Arkansas	911
16	Pennsylvania	905
17	Florida	902
18	Georgia	902
19	South Carolina	890
20	Iowa	863
21	New Mexico	837
22	Delaware	824
23	Maryland	813
24	Texas	808
25	Alabama	803
26	Nevada	768
27	Missouri	748
28	Tennessee	739
29	Minnesota	724
30	Montana	713
31	Wisconsin	697
32	Nebraska	665
33	Kansas	637
34	Colorado	609
35	Ohio	607
36	Idaho	604
37	North Carolina	537
38	Kentucky	521
39	California	512
40	Virginia	499
41	Oklahoma	485
42	West Virginia	485
43	Wyoming	483
44	New Hampshire	416
45	Washington	407
46	Puerto Rico	377
47	Utah	303
48	Oregon	256
49	Alaska	190
50	Hawaii	183
51	Maine	177
52	Vermont	136

New deaths

Table of new deaths per million residents: rolling 3-day average (all 50 states)

Ranking	State	New Deaths Per Million
1	North Dakota	22
2	Rhode Island	17
3	Arkansas	14
4	Nebraska	14
5	Wyoming	13
6	Indiana	10
7	Michigan	10
8	Illinois	9
9	Missouri	9
10	Montana	9
11	Tennessee	9
12	Connecticut	8
13	Idaho	8
14	Kansas	8
15	Delaware	7
16	Louisiana	7
17	Mississippi	7
18	South Dakota	7
19	West Virginia	7
20	Kentucky	6
21	Minnesota	6
22	Nevada	6
23	Pennsylvania	6
24	Wisconsin	6
25	Colorado	5
26	Maryland	5
27	Massachusetts	5
28	Alabama	4
29	Florida	4
30	Iowa	4
31	New Jersey	4
32	New York	4
33	Ohio	4
34	Oklahoma	4
35	Oregon	4
36	South Carolina	4
37	California	3
38	North Carolina	3
39	Texas	3
40	Utah	3
41	Vermont	3
42	Arizona	2
43	District of Columbia	2
44	Georgia	2
45	Maine	2
46	New Mexico	2
47	Puerto Rico	2
48	Virginia	2
49	Alaska	1
50	New Hampshire	1
51	Washington	1
52	Hawaii	0

Counties

• This section focuses on the county level. It shows tables with our counties ranked by percentile of U.S. counties for per population rates of total cases and total deaths.
  • Each table also shows the top five counties in the country in addition to our counties, for added perspecive.
• In addition to the tables, our counties’ percentile for both total cases and total deaths are plotted over time.

Confirmed cases

Table showing total cases per million and percentile for all US counties. Includes our counties and the top 5 in the US for perspective.

County	State	Cases Per Million	Raw Ranking	Percentile
Crowley	Colorado	223,725	1	99
Norton	Kansas	209,849	2	99
Bon Homme	South Dakota	204,028	3	99
Buffalo	South Dakota	200,306	4	99
Lincoln	Arkansas	199,171	5	99
Davidson	Tennessee	73,424	514	83
Richland	South Carolina	52,446	1367	56
York	South Carolina	39,765	2053	34
Orange	California	29,225	2534	19
Pierce	Washington	22,409	2771	11

Our county percentiles over time

Deaths

Table showing total deaths per million and percentile for all US counties. Includes our counties and the top 5 in the US for perspective.

County	State	Deaths Per Million	Raw Ranking	Percentile
Gove	Kansas	7,587	1	99
Jerauld	South Dakota	6,458	2	99
Dickey	North Dakota	5,952	3	99
Foster	North Dakota	5,607	4	99
Gregory	South Dakota	5,496	5	99
Richland	South Carolina	746	1447	53
Davidson	Tennessee	645	1672	46
York	South Carolina	527	1926	38
Orange	California	514	1956	37
Pierce	Washington	380	2283	27

Our county percentiles over time

Raw counts

• This section shows the raw counts over time for the U.S., our states, and our counties for four categories:
  • Total confirmed cases: This is the cumulative total of cases reported.
  • New confirmed cases: This is the number of new cases reported on each respective date.
    • For the U.S. and states, a trendline shows the rolling 7-day average of new cases, to help better visualize the peak.
  • Total deaths: This is the cumulative total of deaths reported.
  • New deaths: This is the number of new deaths reported on each respective date.
    • For the U.S. and states, a trendline shows the rolling 7-day average of new deaths, to help better visualize the peak.

Total confirmed cases

U.S.

Our states

Our counties

New confirmed cases

U.S.

Our states

Our counties

Total deaths

U.S.

Our states

Our counties

New deaths

U.S.

Our states

Our counties

Stay-at-home comparisons

• This section is a work in progress. It’s exploring what effect stay-at-home orders have on spread. It’s inherentely messy because some states have localized orders but not orders for their full state and because states implemented the orders at different times.
  • The first plot shows a rolling 3-day average of new cases per million residents, averaged across 2 groups of states – those with a state-wide stay-at-home order in effect and those without. The size of the dots reflect the number of states in each group for each given day (so the dots are getting bigger over time for the group with orders, as more states implement those orders).
    • So far, this view doesn’t show if these orders are “working” or not. Instead, the biggest takeaway from the current plot is that the states that implemented orders are reacting to having worse spread, which is why the case rate is so much higher at the start for them. As a state decides it has a problem and implements an order, it joins the grouping of states with orders and drives the rate up for that group.
    • Over time (if the policies work), what we’d expect is that the line for the group with orders flattens or declines in relation to the no-order group. It also may become moot if all states issue orders.
    • As states start rescending their orders, I’ll put them back into the no-order group, so the no-order group should beging to expand again.
  • The second plot tracks progress of states by the number of days since orders have been in effect. It includes only those states with orders and shows their average new case growth rate (a rolling 3-day average of daily growth rates) based on how long it’s been since the order went into effect. Again, the size of the dots show how many states are included in each point – i.e., as you go further right, the dots are smaller because less states are that far past their order.
    • I’m working on how to add a baseline to this chart to compare to states without orders, but that’s challenging because there’s no way to fully align timing.